A Mahalanobis Distance-based Approach for Dynamic Multi-objective Optimization with Stochastic Changes

In recent years, researchers have made significant progress in handling dynamic multi-objective optimization problems (DMOPs), particularly for environmental changes with predictable characteristics. However, little attention has been paid to DMOPs stochastic changes. It may be difficult existing evolutionary algorithms (DMOEAs) effectively handle this kind of because most DMOEAs assume that follow regular patterns and consecutive environments are similar. This paper presents a Mahalanobis Distance-based approach (MDA) deal Specifically, we make an all-sided assessment search via distance on saved information learn the relationship between new environment historical ones. Afterward, change response strategy applies learning accelerate convergence maintain diversity population. Besides, degree is considered all decision variables alleviate impact evolving MDA tested 2 4 objectives. The results show performs significantly better than other latest paper, suggesting effective